An investigation of effort distribution among development phases: A four‐stage progressive software cost estimation model. Issue 10 (21st June 2017)

• Record Type: Journal Article
• Authors: Papatheocharous, Efi; Bibi, Stamatia; Stamelos, Ioannis; Andreou, Andreas S.
• Is Part Of: Journal of software. Volume 29:Issue 10(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-21
• Subjects: four‐stage progressive model -- project management -- software cost estimation; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1881
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Software cost estimation is a key process in project management. Estimations in the initial project phases are made with a lot of uncertainty that influences estimation accuracy which typically increases as the project progresses in time. Project data collected during the various project phases can be used in a progressive time‐dependent fashion to train software cost estimation models. Our motivation is to reduce uncertainty and increase confidence based on the understanding of patterns of effort distributions in development phases of real‐world projects. In this work, we study effort distributions and suggest a four‐stage progressive software cost estimation model, adjusting the initial effort estimates during the development life‐cycle based on newly available data. Initial estimates are reviewed on the basis of the experience gained as development progresses and as new information becomes available. The proposed model provides an early, a post‐planning, a post‐specifications, and a post‐design estimate, while it uses industrial data from the ISBSG (R10) dataset. The results reveal emerging patterns of effort distributions and indicate that the model provides effective estimations and exhibits high explanatory value. Contributions in lessons learned and practical implications are also provided. Abstract : The four‐stage progressive model, using artificial neural networks and industrial data from the ISBSG (R10) dataset, provides early, postplanning, postspecifications, and postdesign estimates by adjusting the initial effort estimates during the development life cycle. Initial estimates are reviewed on the basis of experience gained as development progresses and new information becomes available. Results reveal emerging patterns of effort distributions and correlations and exhibit the models' effectiveness and high explanatory value. Contributions in lessons learned and practical implications are provided.